

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ImageGPTImageProcessor



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=7  ,      _lowercase=3  ,      _lowercase=18  ,      _lowercase=30  ,      _lowercase=400  ,      _lowercase=True  ,      _lowercase=None  ,      _lowercase=True  ,      )     -> List[str]:
									a_						:						Optional[Any] 							= size if size is not None else {"""height""": 18, """width""": 18}
									a_						:						Optional[int] 							= parent
									a_						:						Dict 							= batch_size
									a_						:						int 							= num_channels
									a_						:						Union[str, Any] 							= image_size
									a_						:						Optional[int] 							= min_resolution
									a_						:						List[Any] 							= max_resolution
									a_						:						List[Any] 							= do_resize
									a_						:						int 							= size
									a_						:						Union[str, Any] 							= do_normalize





						def 			UpperCamelCase__					(    self    )     -> Dict:
									return {
									    # here we create 2 clusters for the sake of simplicity
									    "clusters": np.asarray(
									        [
									            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
									            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
									        ]    ),
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									}







@require_torch
@require_vision
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Any       					=			ImageGPTImageProcessor if is_vision_available() else None





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						int 							= ImageGPTImageProcessingTester(self    )





						@property
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									return self.image_processor_tester.prepare_image_processor_dict()





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(_lowercase  ,      """clusters"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_resize"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """size"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_normalize"""    )    )





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Any 							= self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size  ,      {"""height""": 18, """width""": 18}    )

									a_						:						Dict 							= self.image_processing_class.from_dict(self.image_processor_dict  ,      size=42    )
									self.assertEqual(image_processor.size  ,      {"""height""": 42, """width""": 42}    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Tuple 							= self.image_processing_class(**self.image_processor_dict    )
									a_						:						str 							= json.loads(image_processor.to_json_string()    )
									for key, value in self.image_processor_dict.items():
												if key == "clusters":
															self.assertTrue(np.array_equal(_lowercase  ,      obj[key]    )    )
												else:
															self.assertEqual(obj[key]  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						List[str] 							= self.image_processing_class(**self.image_processor_dict    )

									with tempfile.TemporaryDirectory() as tmpdirname:
												a_						:						List[Any] 							= os.path.join(_lowercase  ,      """image_processor.json"""    )
												image_processor_first.to_json_file(_lowercase    )
												a_						:						List[str] 							= self.image_processing_class.from_json_file(_lowercase    ).to_dict()

									a_						:						str 							= image_processor_first.to_dict()
									for key, value in image_processor_first.items():
												if key == "clusters":
															self.assertTrue(np.array_equal(_lowercase  ,      image_processor_second[key]    )    )
												else:
															self.assertEqual(image_processor_first[key]  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Union[str, Any] 							= self.image_processing_class(**self.image_processor_dict    )

									with tempfile.TemporaryDirectory() as tmpdirname:
												image_processor_first.save_pretrained(_lowercase    )
												a_						:						Any 							= self.image_processing_class.from_pretrained(_lowercase    ).to_dict()

									a_						:						Optional[Any] 							= image_processor_first.to_dict()
									for key, value in image_processor_first.items():
												if key == "clusters":
															self.assertTrue(np.array_equal(_lowercase  ,      image_processor_second[key]    )    )
												else:
															self.assertEqual(image_processor_first[key]  ,      _lowercase    )





						@unittest.skip("""ImageGPT requires clusters at initialization"""    )
						def 			UpperCamelCase__					(    self    )     -> int:
									pass







def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						List[str] 							= load_dataset("""hf-internal-testing/fixtures_image_utils"""  ,	split="""test""")

			a_						:						List[str] 							= Image.open(dataset[4]["""file"""])
			a_						:						str 							= Image.open(dataset[5]["""file"""])

			a_						:						Tuple 							= [imagea, imagea]

			return images



@require_vision
@require_torch
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Tuple 							= ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""    )

									a_						:						int 							= prepare_images()

									# test non-batched
									a_						:						Dict 							= image_processing(images[0]  ,      return_tensors="""pt"""    )

									self.assertIsInstance(encoding.input_ids  ,      torch.LongTensor    )
									self.assertEqual(encoding.input_ids.shape  ,      (1, 1_024)    )

									a_						:						Tuple 							= [306, 191, 191]
									self.assertEqual(encoding.input_ids[0, :3].tolist()  ,      _lowercase    )

									# test batched
									a_						:						Dict 							= image_processing(_lowercase  ,      return_tensors="""pt"""    )

									self.assertIsInstance(encoding.input_ids  ,      torch.LongTensor    )
									self.assertEqual(encoding.input_ids.shape  ,      (2, 1_024)    )

									a_						:						Optional[Any] 							= [303, 13, 13]
									self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,      _lowercase    )





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			with open(a__) as metadata_file:
						a_						:						Any 							= json.load(a__)
			a_						:						Dict 							= LukeConfig(use_entity_aware_attention=a__  ,	**metadata["""model_config"""])

			# Load in the weights from the checkpoint_path
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")

			# Load the entity vocab file
			a_						:						List[str] 							= load_entity_vocab(a__)

			a_						:						int 							= RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""])

			# Add special tokens to the token vocabulary for downstream tasks
			a_						:						Optional[Any] 							= AddedToken("""<ent>"""  ,	lstrip=a__  ,	rstrip=a__)
			a_						:						int 							= AddedToken("""<ent2>"""  ,	lstrip=a__  ,	rstrip=a__)
			tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]})
			config.vocab_size += 2

			print(f'''Saving tokenizer to {pytorch_dump_folder_path}''')
			tokenizer.save_pretrained(a__)
			with open(os.path.join(a__  ,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""])  ,	"""w""") as f:
						json.dump(a__  ,	a__)

			a_						:						List[str] 							= LukeTokenizer.from_pretrained(a__)

			# Initialize the embeddings of the special tokens
			a_						:						Optional[int] 							= state_dict["""embeddings.word_embeddings.weight"""]
			a_						:						List[str] 							= word_emb[tokenizer.convert_tokens_to_ids(["""@"""])[0]].unsqueeze(0)
			a_						:						List[Any] 							= word_emb[tokenizer.convert_tokens_to_ids(["""#"""])[0]].unsqueeze(0)
			a_						:						Optional[Any] 							= torch.cat([word_emb, ent_emb, enta_emb])

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers):
						for matrix_name in ["query.weight", "query.bias"]:
									a_						:						Any 							= f'''encoder.layer.{layer_index}.attention.self.'''
									a_						:						List[str] 							= state_dict[prefix + matrix_name]
									a_						:						List[Any] 							= state_dict[prefix + matrix_name]
									a_						:						Dict 							= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			a_						:						str 							= state_dict["""entity_embeddings.entity_embeddings.weight"""]
			a_						:						int 							= entity_emb[entity_vocab["""[MASK]"""]]

			a_						:						int 							= LukeModel(config=a__).eval()

			a_							,					a_						:						Optional[int] 							= model.load_state_dict(a__  ,	strict=a__)
			if not (len(a__) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f'''Missing keys {", ".join(a__)}. Expected only missing embeddings.position_ids''')
			if not (all(key.startswith("""entity_predictions""") or key.startswith("""lm_head""") for key in unexpected_keys)):
						raise ValueError(
						    """Unexpected keys"""
						    f''' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions") or key.startswith("lm_head"))])}''')

			# Check outputs
			a_						:						List[Any] 							= LukeTokenizer.from_pretrained(a__  ,	task="""entity_classification""")

			a_						:						str 							= (
			    """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
			    """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
			)
			a_						:						str 							= (3_9, 4_2)
			a_						:						Tuple 							= tokenizer(a__  ,	entity_spans=[span]  ,	add_prefix_space=a__  ,	return_tensors="""pt""")

			a_						:						List[str] 							= model(**a__)

			# Verify word hidden states
			if model_size == "large":
						a_						:						Optional[int] 							= torch.Size((1, 4_2, 1_0_2_4))
						a_						:						List[str] 							= torch.tensor(
						    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]])
			else:  # base
						a_						:						List[str] 							= torch.Size((1, 4_2, 7_6_8))
						a_						:						str 							= torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''')
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						a_						:						Dict 							= torch.Size((1, 1, 1_0_2_4))
						a_						:						int 							= torch.tensor([[0.0466, -0.0106, -0.0179]])
			else:  # base
						a_						:						Optional[Any] 							= torch.Size((1, 1, 7_6_8))
						a_						:						str 							= torch.tensor([[0.1457, 0.1044, 0.0174]])

			if not (outputs.entity_last_hidden_state.shape != expected_shape):
						raise ValueError(
						    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
						    f''' {expected_shape}''')
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]  ,	a__  ,	atol=1e-4):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print("""Saving PyTorch model to {}""".format(a__))
			model.save_pretrained(a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= {}
			with open(a__  ,	"""r"""  ,	encoding="""utf-8""") as f:
						for index, line in enumerate(a__):
									a_							,					a_						:						List[Any] 							= line.rstrip().split("""\t""")
									a_						:						Any 							= index

			return entity_vocab


if __name__ == "__main__":
	__snake_case				:      Tuple   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
	parser.add_argument(
	    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
	)
	parser.add_argument(
	    """--entity_vocab_path""",
	    default=None,
	    type=str,
	    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
	)
	parser.add_argument(
	    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
	)
	__snake_case				:      List[str]   		=			parser.parse_args()
	convert_luke_checkpoint(
	    args.checkpoint_path,
	    args.metadata_path,
	    args.entity_vocab_path,
	    args.pytorch_dump_folder_path,
	    args.model_size,
	)


